Methods and systems for simulated operation of an electric vertical take-off and landing (evtol) aircraft

ABSTRACT

Aspects relate to augmented reality (AR) methods and systems for simulated operation of an electric vertical take-off and landing (eVTOL) aircraft. An exemplary AR system includes at least an aircraft component of an eVTOL aircraft, a computing device configured to operate a flight simulator to simulate flight in an environment and simulate at least a virtual representation interactive with the flight simulator, where the at least a virtual representation includes an aircraft digital twin of the at least an aircraft component, and a mesh network configured to communicatively connect the at least an aircraft component and the computing device and communicate encrypted data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of Non-provisional application Ser.No. 17/348,916 filed on Jun. 16, 2021 and entitled “METHODS AND SYSTEMSFOR SIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING(EVTOL) AIRCRAFT,” the entirety of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention generally relates to the field of computermodeling and simulation. In particular, the present invention isdirected to simulated operation of an electric vertical take-off andlanding (eVTOL) aircraft.

BACKGROUND

Aircraft simulators may aid in training, maintenance, and testing ofaircraft. The functionality of aircraft simulators may be limited bytheir accuracy of representation for aircraft.

SUMMARY OF THE DISCLOSURE

In an aspect an augmented reality (AR) system for simulated operation ofan electric vertical take-off and landing (eVTOL) aircraft includes atleast an aircraft component of an electric vertical take-off and landing(eVTOL) aircraft, a computing device configured to operate a flightsimulator to simulate flight in an environment and simulate at least avirtual representation interactive with the flight simulator, where theat least a virtual representation includes an aircraft digital twin ofthe at least an aircraft component, and a mesh network configured tocommunicatively connect the at least an aircraft component and thecomputing device and communicate encrypted data.

In another aspect an augmented reality (AR) method of simulatedoperation of an electric vertical take-off and landing (eVTOL) aircraftincludes operating, using a device computing, a flight simulator tosimulate flight in an environment, simulating, using the computingdevice, at least a virtual representation interactive with the flightsimulator, where the at least a virtual representation includes anaircraft digital twin representing at least an aircraft component of anelectric vertical take-off and landing (eVTOL) aircraft, communicativelyconnecting, using a mesh network, the at least an aircraft component andthe computing device, and communicating, using the mesh network,encrypted data.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary system for wrapping simulatedintra-aircraft communication to a physical controller area network;

FIG. 2 is a diagrammatic representation illustrating a plurality ofphysical controller area network buses;

FIG. 3 is a diagrammatic representation illustrating a transmissionsignal from a controller area network;

FIG. 4 is a graphical representation illustrating a controller areanetwork signal transduction;

FIG. 5 is a diagrammatic representation of an electric aircraft;

FIG. 6 is a block diagram of an exemplary digital twin for an eVTOLaircraft;

FIG. 7 is a block diagram of an exemplary avionic mesh network;

FIG. 8 is a block diagram of an exemplary flight controller;

FIG. 9 is a block diagram of an exemplary machine-learning process;

FIG. 10 is a flow diagram of an exemplary method of wrapping simulatedintra-aircraft communication to a physical controller area network; and

FIG. 11 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for simulated operation of an electric verticaltake-off and landing (eVTOL) aircraft. In an embodiment, a digital twinmay be used to represent at least an aircraft component of an eVTOLaircraft.

Aspects of the present disclosure can be used to maintain and use adigital twin in conjunction with a flight simulator and/or a simulatormodule. Aspects of the present disclosure can also be used tocommunicate aircraft and/or simulation data between at least a digitaltwin and at least an aircraft component and vice versa. This is so, atleast in part, to ensure accuracy of aircraft simulation for training,testing, and maintenance purposes.

Aspects of the present disclosure allow for simulated operation of aneVTOL aircraft, as well as simulated maintenance and testing of an eVTOLaircraft. Exemplary embodiments illustrating aspects of the presentdisclosure are described below in the context of several specificexamples.

Referring now to FIG. 1 , an exemplary embodiment of an augmentedreality (AR) system 100 for simulated operation of an electric verticaltake-off and landing (eVTOL) aircraft is shown. System 100 includes acomputing device 104. As used in this disclosure, “augmented reality”refers to any technology used to alter, add-to, remove, supplant, orotherwise modify a perceived realty, for instance of a user;accordingly, augmented reality may include mixed reality and/or virtualreality. In some cases, augmented reality may include visual and/orauditory stimuli. Alternatively or additionally, augmented reality mayinclude non-visual modalities, for example haptic somata sensory andolfactory. In some cases, augmented reality may include virtual realityand/or mixed reality visual modality and one or more physical aircraftelements, to emulate a flight environment and/or functional aircraft.Computing device 104 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Computing device 104 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. Computing device104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device104 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Computing device 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Computing device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1 , computing device 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device 104 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

With continued reference to FIG. 1 , computing device 104 may beconfigured to operate a flight simulator 108. As used in thisdisclosure, a “flight simulator” is a program or set of operations thatsimulate flight. In some cases, a flight simulator may simulate flightwithin an environment, for example an environmental atmosphere in whichaircraft fly, airports at which aircraft take-off and land, and/ormountains and other hazards aircraft attempt to avoid crashing into. Insome cases, an environment may include geographical, atmospheric, and/orbiological features. In some cases, a flight simulator 108 may model anartificial and/or virtual aircraft in flight as well as an environmentin which the artificial and/or virtual aircraft flies. In some cases, aflight simulator 108 may include one or more physics models, whichrepresent analytically or through data-based, such as without limitationmachine-learning processes, physical phenomenon. Physical phenomenon maybe associated with an aircraft and/or an environment. For example, someversions of a flight simulator 108 may include thermal modelsrepresenting aircraft components by way of thermal modeling. Thermalmodeling techniques may, in some cases, include analyticalrepresentation of one or more of convective hear transfer (for exampleby way of Newton's Law of Cooling), conductive heat transfer (forexample by way of Fourier conduction), radiative heat transfer, and/oradvective heat transfer. In some cases, flight simulator 108 may includemodels representing fluid dynamics. For example, in some embodiments,flight simulator may include a representation of turbulence, wind shear,air density, cloud, precipitation, and the like. In some embodiments,flight simulator 108 may include at least a model representing opticalphenomenon. For example, flight simulator may include optical modelsrepresentative of transmission, reflectance, occlusion, absorption,attenuation, and scatter. Flight simulator 108 may includenon-analytical modeling methods; for example, the flight simulator mayinclude, without limitation, a Monte Carlo model for simulating opticalscatter within a turbid medium, for example clouds. In some embodiments,a flight simulator 108 may represent Newtonian physics, for examplemotion, pressures, forces, moments, and the like. An exemplary flightsimulator may include Microsoft Flight Simulator from Microsoft ofRedmond, Washington, U.S.A.

With continued reference to FIG. 1 , system 100 may include a network112. Network may include any network described in this disclosure, forexample without limitation an avionic mesh network as described below.Network 112 may facilitate communicative connection between two or moredevices. “Communicatively connected”, for the purposes of thisdisclosure, is a process whereby one device, component, or circuit isable to receive data from and/or transmit data to another device,component, or circuit; communicative connection may be performed bywired or wireless electronic communication, either directly or by way ofone or more intervening devices or components. In an embodiment,communicative connection includes electrically coupling an output of onedevice, component, or circuit to an input of another device, component,or circuit. Communicative connecting may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, optical coupling, or the like. Insome cases, network 112 may include a mesh network. In some cases,network 112 may communicated encrypted data. As used in this disclosure,“encrypted data” is any communicable information that is protected orsecured by any method, including obfuscation, encryption, and the like.Encrypted data may include information protected by any cryptographicmethod described in this disclosure, for example in reference to FIG. 7. In some embodiments, network 112 may include an intra-aircraft networkand/or an inter-aircraft network. Intra-aircraft network may include anyintra-aircraft network described in this disclosure. Inter-aircraftnetwork may include any inter-aircraft network described in thisdisclosure.

With continued reference to FIG. 1 , network 112 may be communicativelyconnected to at least an aircraft component 116. As used in thisdisclosure, an “aircraft component” is any component of an aircraft, forexample an electric vertical and take-off (eVTOL) aircraft. In someembodiments, an aircraft component may include, without limitation, anyof a flight component, a pilot input, a pilot display, a sensor, anactuator, a flight surface, an inverter, and a motor. An exemplaryaircraft component 132 may include an integrated electric propulsionunit. Disclosure related to systems and methods of use for integratedelectric propulsion units include U.S. patent application Ser. No.16/703,225 entitled “INTEGRATED ELECTRIC PROPULSION ASSEMBLY,”incorporated herein by reference in its entirety. Aircraft component 132may be configured to receive simulated signal 120 from at least acontroller area network 128, for example by way of a port. In someembodiments, at least a controller area network 128 may include acontroller area network bus and/or a plurality of controller areanetwork buses. In some embodiments, aircraft component 132 may beconfigured to respond to simulated signal 120. In some cases, computingdevice 104 and at least an aircraft component 116 may be configured tocommunicate by way of network 112. For example, in some cases, computingdevice 104 may be configured to transmit encrypted data to at least anaircraft component 116. Alternatively or additionally, in some cases,computing device 104 may be configured to receive encrypted data from atleast an aircraft component 116.

With continued reference to FIG. 1 , in some embodiments, system 100 mayadditionally include a simulator module 120. Simulator module 120 may becommunicatively connected to computing device 104 by any communicationmeans described in this disclosure, for example without limitationnetwork 112. As used in this disclosure, a “simulator module” is aphysical component that is a simulation of an aircraft component.Simulator module may include actual aircraft components that have beenseparated from a functioning aircraft or otherwise de-activated. Asimulator module may include a model or replica. In some cases,simulator module may include a physical twin of at least an aircraftcomponent. In some cases, simulator module may include a physicalcockpit 124. A physical cockpit 124 may include at least an aircraftcomponent. For example, a physical cockpit 124 may include one or moreof an aircraft interior, seating, windows, displays, pilot controls, andthe like. A physical cockpit 124 may be used to perform a simulatedflight mission. As used in this disclosure, a “simulated flight mission”is any use of a flight simulator 108 that includes a simulated flight.Simulator module 120 and/or physical cockpit 124 may include at least apilot control 128 configured to interface with a user. Pilot control 128may include any pilot control described in this disclosure. In somecases, at least one of simulator module 120, physical cockpit 124, andpilot control 128 may include at least a sensor 132. At least a sensor132 may be communicatively connected to computing device 104. In somecases, at least a sensor 132 may be configured to detect a userinteraction with the at least a pilot control 128. At least a sensor 132may include any sensor described in this disclosure.

With continued reference to FIG. 1 , computing device may be configuredto simulate at least a virtual representation 136. As described in thisdisclosure, a “virtual representation” includes any model or simulationaccessible by computing device which is representative of a physicalphenomenon, for example without limitation at least an aircraftcomponent 116 or simulator module 120. In some cases, virtualrepresentation may be interactive with flight simulator 108. Forexample, in some cases, data may originate from virtual representationand be input into flight simulator 108. Alternatively or additionally,in some cases, virtual representation 136 may modify or transform dataalready available to flight simulator 108. Virtual representation 136may include an aircraft digital twin 140 of at least an aircraftcomponent 116. Aircraft digital twin 140 may include any digital twin asdescribed in this disclosure, for example below. In some cases, at leastan aircraft component 116 includes an electric vertical take-off andlanding (eVTOL) aircraft, for example a functional flight-worthy eVTOLaircraft; and aircraft digital twin 140 is a digital twin of the eVTOLaircraft. In some cases, at least a virtual representation 136 mayinclude a virtual controller area network. Virtual controller areanetwork may include any virtual controller area network as described inthis disclosure, for example below. In some cases, aircraft digital twinmay include a flight controller model. Flight controller model mayinclude any flight controller model described in this disclosure.

Still referring to FIG. 1 , in some embodiments, virtual representation136 may additionally include a simulator digital twin 144. Simulationdigital twin may include any digital twin as described in thisdisclosure, for example below. Simulator digital twin 144 may representat least a portion of simulator module 120, for instance at least acomponent of simulator module 120, such as without limitation, physicalcockpit 124, pilot control 128, and at least a sensor 132. In somecases, network 112 may be additionally configured to communicativelyconnect simulator module 120 with at least an aircraft component 116 andcomputing device 104.

With continued reference to FIG. 1 , in some embodiments, at least anaircraft component may be communicatively connected using at least acontroller area network. At least a controller area network may includea plurality of physical controller area network buses communicativelyconnected to the aircraft, such as an electronic vertical take-off andlanding (eVTOL) aircraft as described in further detail below. Aphysical controller area network bus may be vehicle bus unit including acentral processing unit (CPU), a CAN controller, and a transceiverdesigned to allow devices to communicate with each other's applicationswithout the need of a host computer which is located physically at theaircraft. Physical controller area network (CAN) bus unit may includephysical circuit elements that may use, for instance and withoutlimitation, twisted pair, digital circuit elements/FGPA,microcontroller, or the like to perform, without limitation, processingand/or signal transmission processes and/or tasks; circuit elements maybe used to implement CAN bus components and/or constituent parts asdescribed in further detail below. Physical CAN bus unit may includemultiplex electrical wiring for transmission of multiplexed signaling.Physical CAN bus unit may include message-based protocol(s), wherein theinvoking program sends a message to a process and relies on that processand its supporting infrastructure to then select and run appropriateprograming. A plurality of physical CAN bus units located physically atthe aircraft may include mechanical connection to the aircraft, whereinthe hardware of the physical CAN bus unit is integrated within theinfrastructure of the aircraft. Physical CAN bus units may becommunicatively connected to the aircraft and/or with a plurality ofdevices outside of the aircraft, as described in further detail below.

Still referring to FIG. 1 , a plurality of physical CAN bus unitscommunicatively connected to an aircraft may include flightcontroller(s), battery terminals, gyroscope, accelerometer,proportional-integral-derivative controller, and the like, which maycommunicate directly with one another and to operating flight controldevices, virtual machines, and other computing devices elsewhere.Physical CAN bus units may be mechanically connected to each otherwithin an aircraft wherein physical infrastructure of the device isintegrated into the aircraft for control and operation of variousdevices within the aircraft. Physical CAN bus unit may becommunicatively connected with each other and/or to one or more otherdevices, such as via a CAN gateway. Communicatively connecting mayinclude direct electrical wiring, such as is done within automobiles andaircraft. Communicatively connecting may include infrastructure forreceiving and/or transmitting transmission signals, such as with sendingand propagating an analogue or digital signal using wired, optical,and/or wireless electromagnetic transmission medium.

Continuing in reference to FIG. 1 , a plurality of physical CAN busunits communicatively connected to aircraft may receive pilot input.Pilot input may include input using a throttle lever, inceptor stick,collective pitch control, steering wheel, brake pedals, pedal controls,toggles, joystick. One of ordinary skill in the art, upon receiving thebenefit of this disclosure in its entirety, may appreciate the varietyof pilot input controls that may be present in an electric aircraftconsistent with the present disclosure. For instance and withoutlimitation, inceptor stick may be consistent with disclosure of inceptorstick in U.S. patent application Ser. No. 17/001,845 and titled “A HOVERAND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which isincorporated herein by reference in its entirety. In furthernon-limiting illustrative examples, a collective pitch control may beconsistent with disclosure of collective pitch control in U.S. patentapplication Ser. No. 16/929,206 and titled “HOVER AND THRUST CONTROLASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated herein byreference in its entirety. Pilot input control 104 may be physicallylocated within the aircraft or located remotely outside the aircraft ina second location communicatively connected to at least a portion of theaircraft.

Continuing in in reference to FIG. 1 , each physical CAN bus unit may beconfigured to detect a measured state datum of a plurality of measuredstate data of aircraft. A “measured state datum,” as used in thisdisclosure, is a datum that is collected via a CAN describing somefunctionality about aircraft. Measured state data may include aplurality of data signals detailing a control to one or more actuatorscommunicatively connected to the aircraft. Measured state data mayinclude a plurality of data entries relating aircraft pitch, roll, yaw,torque, angular velocity, climb, speed, performance, lift, thrust, drag,battery charge, fuel level, location, and the like. Measured state datamay include a plurality of data communicating the status of flightcontrol devices such as proportional-integral-derivative controller,fly-by-wire system functionality, aircraft brakes, impeller, artificialfeel devices, stick shaker, power-by-wire systems, active flow control,thrust vectoring, alerion, landing gear, battery pack, propulsor,management components, control surfaces, sensors/sensor suites, creaturecomforts, inceptor, throttle, collective, cyclic, yaw pedals, MFDs,PFDs, and the like. Measured state data may exist as analogue and/ordigital data, originating from physical CAN bus units such as bits,where a series of serial binary data are composed and transmittedrelaying a measured state as indicated from a device located within, on,or communicating with aircraft.

With continued reference to FIG. 1 , at least an aircraft component 116may transmit a signal using network 112 to computing device 104. Signalmay be received by computing device 104. Computing device 104 mayconvert signal to aircraft data for inputting to flight simulator 108and/or virtual representation 136. Computing device 104 may inputaircraft data to flight simulator 108 and/or virtual representation 136.In some embodiments, at least an aircraft component 116 may include atleast a sensor. A sensor may include any sensor described herein, forexample without limitation an inertial measurement sensor, an analogsensor, a digital sensor, a thermometer, a pressure sensor, a humiditysensor, and the like. In some cases, sensor may be configured to sense acharacteristic associated with an aircraft flight and transduce a signalas a function of the characteristic. An aircraft flight may include anyflight of an aircraft, for instance an eVTOL aircraft. In someembodiments, at least an aircraft component 116 may include at least apilot control. Pilot control may be any pilot control described in thisdisclosure, for example below.

Continuing in reference to FIG. 1 , a physical CAN bus unit may becommunicatively connected to an actuator. For example, CAN may beconnected to at least an aircraft component 116 that comprises anactuator. An “actuator,” as used in this disclosure, is a device whichreceives control signals in an aircraft. Actuator may be communicativelyconnected to CAN. Actuator may include a computing device or pluralityof computing devices consistent with the entirety of this disclosure.Actuator may be designed and/or configured to perform any method, methodstep, or sequence of method steps in any embodiment described in thisdisclosure, in any order and with any degree of repetition. Forinstance, actuator may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Actuator may perform any step or sequence of steps as describedin this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 1 , in some embodiments, computing device 104may additionally include or be configured to perform operationsfunctioning a virtual controller area network. In some cases, a virtualcontroller area network may be usefully considered as a component ofaircraft simulator 108 and/or virtual representation 136. Alternativelyor additionally, virtual controller area network may be considereddistinct from aircraft simulator; for example, virtual controller areanetwork may be considered an interpreter of flight simulator 108 into avirtual communication protocol analogous to that of a controller areanetwork. In some cases a virtual CAN may include at least a virtualcontroller area network bus unit configured to receive a transmissionsignal originating from at least a network switch. A virtual controllerarea network bus unit may be a device including a central processingunit (CPU), CAN controller, and transceiver, which receives atransmission signal and virtually recapitulates a message encoded withinthe signal, wherein the message may include without limitation a status,behavior, and/or data of and/or originating from CAN. Virtual CAN busunit may include any physical circuit elements suitable for use in aphysical CAN bus unit as described above. Virtual CAN bus unit mayinclude a multiplexor, multiplexing logic, and/or multiplex electricalwiring for transmission of multiplexed signaling. In some cases, virtualCAN bus may be communicative with a network switch. Virtual CAN bus unitmay include message-based protocol(s), wherein the invoking programsends a message to a process and relies on that process and itssupporting infrastructure to then select and run appropriate programing.Virtual CAN bus unit may include a computing device, as described infurther detail below. Virtual CAN bus unit may include a computer,“smartphone”, IoT device, tablet computer, among other devices withcapability described herein. Virtual CAN bus unit may receive atransmission signal. Virtual CAN bus unit may receive a transmissionsignal as an ethernet transmission signal and/or RF transmission signal.Virtual CAN bus unit may include a virtual machine, which operates as anemulation of a computer system, providing functionality of a physicalcomputer. Virtual CAN bus unit may include any device herein configuredto demultiplex signal, store to disc, transmit signals to other device,and/or send back to flight CAN(s).

Continuing in reference to FIG. 1 , virtual CAN bus unit may beconfigured to demultiplex an incoming transmission signal into aplurality of outgoing messages originating from the plurality ofphysical controller area network buses. An outgoing message may be ademultiplexed transmission signal which originated as part of anincoming transmission signal. Outgoing message may include a pluralityof data, and/or discrete portions thereof. Outgoing message may includeanalogue and/or digital transmission signals, including ethernettransmission signal and/or RF transmission signal. Demultiplexing mayinclude processes of reconverting a transmission signal containing, forexample containing multiple analogue and/or digital signal streams fromat least an aircraft component 116, computing device 104, and/orsimulator module 120, back into original separate and unrelated signalsoriginally relayed from controller area network. Demultiplexing mayinclude extracting original channels on a receiving end to identifywhich physical CAN bus unit a signal originates from. Demultiplexing maybe performed using a demultiplexer such as a binary decoder, or anyprogrammable logic device. Demultiplexing may be performed using acomputing software operating on the virtual CAN bus unit, which maydeconvolute a signal. Alternatively or additionally, virtual CAN busunit may be configured to communicatively connect to each controllerarea network gateway of a plurality of controller area network gateways.Virtual CAN bus unit may receive signal transduction directly from CANnetwork gateways, circumventing the need for multiplexing.

Continuing in reference to FIG. 1 , a virtual CAN bus unit may beconfigured to bridge a plurality of virtual controller area network busunits to a plurality of physical controller area network bus units. Aplurality of virtual controller area network bus units may include atleast a second virtual CAN bus unit aside from a first virtual CAN busunit 132 which originally received a transmission signal. Plurality ofvirtual controller area network bus units may include any capability asdescribed for virtual CAN bus unit herein. A network bridge may includea computer networking device (e.g., virtual CAN bus unit) that creates asingle, aggregate network from multiple communication networks ornetwork segments (e.g., plurality of virtual CAN buses). Networkbridging is distinct from routing. Routing may allow multiple networksto communicate independently and yet remain separate, whereas bridgingmay connect two separate networks as if they were a single network. Inthis way, a virtual CAN bus unit may transmit a demultiplexed outgoingmessages to a plurality of virtual CAN bus units which may operate as ifthey were all part of a single virtual machine. Bridging may include anytype of network bridging technology, such as simple bridging, multiportbridging, and learning or transparent bridging. Virtual CAN bus unit mayperform bridging using a forwarding information base stored incontent-addressable memory (CAM), wherein for each received ethernetframe, virtual CAN bus unit may learn from the frame's source MACaddress and add this together with an interface identifier. Virtual CANbus unit may then forward frame to an interface found on the CAN basedon the frame's destination MAC address. If destination address isunknown, switch may send frame out on all interfaces (except an ingressinterface). This process is oftentimes referred to unicast flooding.Once a bridge learns an addresses of its connected nodes, it may forwarddata link layer frames using a layer-2 forwarding method. There areseveral forwarding methods a bridge can use, for instance and withoutlimitation, store and forward, cut through, fragment free, and adaptiveswitching, of which some methods are performance-increasing methods whenused on “switch” products with the same input and output portbandwidths.

Continuing in reference to FIG. 1 , bridging may include using anydevice that is capable for communicating with a virtual CAN bus unit,computing device, or able to receive data, retrieve data, store data,and/or transmit data, for instance via a data network technology such as3G, 4G/LTE, 5G, Wi-Fi, IEEE 802.11 family standards, IEEE 802.1aqstandards, and the like. For instance and without limitation, ShortestPath Bridging (SPB), specified in the IEEE 802.1aq standard, is acomputer networking technology intended to simplify the creation andconfiguration of networks, while enabling multipath routing. It mayinclude a proposed replacement for Spanning Tree Protocol (SPB) whichblocks any redundant paths that could result in a layer 2 loop. SPB mayallow all paths to be active with multiple equal-cost paths. SPB mayalso increase the number of VLANs allowed on a layer-2 network. Bridgingbetween devices may also include devices that communicate using othermobile communication technologies, or any combination thereof, forinstance and without limitation, short-range wireless communication forinstance, using Bluetooth and/or Bluetooth LE standards, AirDrop,near-field (NFC), and the like. Bridging between devices may beperformed using any wired, optical, or wireless electromagnetictransmission medium, as described herein.

Continuing in reference to FIG. 1 , bridging a plurality of virtualcontroller area network bus units to at least a controller area networkmay include transmitting at least a control message of a plurality ofcontrol messages originating from at least a virtual controller networkbus of the plurality of virtual controller network buses to the at leasta CAN. A control message may include a transmission signal that isintended to control a device that is communicative by way of at least aCAN. A control message may include an output message originating fromcomputing device 104 for modulating an aspect of flight control via adevice communicatively connected to at least a CAN. Control message mayenable a virtual machine, such as virtual CAN bus unit, computing device104, and/or any device described herein, to propagate a transmissiontargeted to at least an aircraft component 116 to effect, actuate,and/or modulate an aircraft mechanism. Control message may include atransmission signal to alter fly-by-wire control, flight control,thrust, angular velocity, climb, altitude, pitch, yaw, roll,acceleration, braking, landing gear mechanism, among other flightcontrols. Control message may include analogue or digital transmissionsignals intended to be displayed to and/or for a pilot operatingaircraft. Control message may include digital messages intended to bedisplayed via a heads-up device (HUD), touch screen, computer, or otherdigital messaging intended to be displayed in the aircraft. Controlmessage may include transmitted signals intended to operate a payloadassociated with aircraft, for instance for releasing a mechanism fordropping a cargo load. Control message may be propagated and transmittedfrom virtual buses using an analogue and/or digital signal via a wired,optical, and/or wireless electromagnetic medium (such as via an ethernetconnection, radio frequency, or any other electromagnetic signaltransmission). Continuing in reference to FIG. 1 , bridging performed bycomputing device 104, simulator module 120, and/or virtual CAN bus unitmay include transmitting at least a control message to control at leastan aircraft component 116. As described herein, actuators may receivesignals, such as control message, for controlling a devicecommunicatively connected with aircraft. Control message, for instanceand without limitation, may signal actuator to control thrustercontrols, landing gear, inceptor, throttle, collective, cyclic,impeller, alerion, rotors, motor, flight display, gyroscope,accelerometer, sensor/sensor suite, fault detection system, inertialmeasure unit (IMU), power management system, air conditioning/heat,among other flight controls, displays, and/or devices. Control messagemay be received by all systems communicative with at least a CANdemultiplexed and bridged to computing device 104, at least an aircraftcomponent 116, and the like. Control message may originate from anydevice which is bridged via virtual CAN bus unit, for example computingdevice 104 and/or flight simulator 108. Additionally disclosure relatedto virtual and physical CAN buses is detailed in U.S. patent applicationSer. No. 17/218,342 entitled “METHOD AND SYSTEM FOR VIRTUALIZAING APLURALITY OF CONTROLLER AREA NETWORK BUS UNITS COMMUNICATIVELY CONNECTEDTO AN AIRCRAFT,” by J. Auerbach et al., which is incorporated herein byreference in its entirety.

With continued reference to FIG. 1 , system 100 may include a display148 communicatively connected to computing device 104. In some cases,display 148 may be configured to display at least a virtualrepresentation 136. In some cases, display 148 may be configured todisplay at least a graphical element of flight simulator 108. Display148 may include any display technology known in the art, including thosefor instance disclosed with reference to FIG. 11 . In some embodiments,display 148 may include a plurality of displays and may be configured todisplay imagery that is immersive to a user. For example, in some cases,display 148 may include a curved screen or set of screens that cover afield of vision. In some embodiments, display 148 may be configured todisplay a field of vision extending peripherally to cover some or all ofthe field of vision possible from a cockpit of an aircraft. In somecases, display 148 may include an Omnimax or Imax screen. In some cases,display 148 may include a projector, for example red, green, blue, (RGB)projectors and the like. In some cases, display 148 may include multiplescreens, which may be joined together to form a larger screen withvarious possible geometric configurations. In some cases, display 148may include multiple projectors. In some cases, display 148 may includecircuitry, hardware, firmware, and/or software to coordinate imagedisplay using multiple screens/projectors. For example, circuitry,hardware, firmware, and/or software may be configured to overlap displayzones or views from multiple displays, screens, projectors, and thelike.

With continued reference to FIG. 1 , in some cases, display 148 mayinclude a stereoscopic display. A “stereoscopic display” as used in thisdisclosure, is a display 148 that simulates a user experience of viewinga three-dimensional space and/or object, for instance by simulatingand/or replicating different perspectives of a user's two eyes; this isin contrast to a two-dimensional image, in which images presented toeach eye are substantially identical, such as may occur when viewing aflat screen display. Stereoscopic display 148 may display two flatimages having different perspectives, each to only one eye (i.e.,parallax), which may simulate the appearance of an object or space asseen from the perspective of that eye. Alternatively or additionally,stereoscopic display 148 may include a three-dimensional display 148such as a holographic display 148 or the like. In some embodiments,display 148 may include an autostereoscopic display. In some cases, anautostereoscopic display may include a single screen that projects twoor more views, which are relayed to different eyes of a viewer, forexample without limitation by way of lenticular lenses. In some cases,an autostereoscopic display may include adaptive optics elements, suchas adaptive lenticular lenses using indium tin oxide electrodes and aliquid crystal cell, to adjust optical properties of the lenticular lensaccording to a sensed position of a user's eyes. In some cases, aneye-tracking system, for example a system including an eye-trackingcamera, may be used to determine a location of a user's eyes (e.g.,pupils) relative a display 148 and adjust adaptive optics and displayparameters accordingly. In some cases, an autostereoscopic display mayproject multiple views for multiple pairs of eyes, such that differentviews are viewable from different locations relative display 148. Insome exemplary cases, an autostereoscopic display having a staticlenticular lens screen may project 7 different views. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various alternative or additional types of stereoscopic display 132that may be employed in augmented reality device 104. In some cases,display 148 may include a display usable with a headset, for example anaugmented reality or virtual reality headset. For example, in somecases, display 148 may include a liquid crystal display and/or aheads-up display. Headset may include a screen that displays a field ofvision to user. System 100 may include a projection device, defined as adevice that inserts images into field of vision. Projection device mayinclude a software and/or hardware component that adds inserted imagesinto a display 148 signal to be rendered on the display 148. Projectiondevice and/or display may make use of reflective waveguides, diffractivewaveguides, or the like to transmit, project, and/or display images. Forinstance, and without limitation, projection device and/or display 148may project images through and/or reflect images off an eyeglass-likestructure and/or lens piece, where either both field of vision andimages from projection device may be so displayed, or the former may bepermitted to pass through a transparent surface. Projection deviceand/or display 148 may be incorporated in a contact lens or eye tapdevice, which may introduce images into light entering an eye to causedisplay of such images. Projection device and/or display 148 may displaysome images using a virtual retina display 132 (VRD), which may displayan image directly on a retina of user.

Referring now to FIG. 2 , a non-limiting exemplary embodiment 200 of aplurality of physical controller area network buses are illustrated. CANmay be used to prevent the need for large, multi-core wiring harnessesused in eVTOL aircraft. CAN bus speed my may reach 1 Mbit/sec, which maybe achieved with a bus length of up to 40 meters when using a twistedwire pair. The bus must be terminated at each end, typically using aresistor of 120 Ohms. For bus lengths longer than 40 meters the busspeed must be reduced, for instance, 1000-meter bus may be achieved witha 50 Kbit/sec bus speed. Aircraft may include a plurality of sensorsthat connect with physical CAN bus units layers to transmit signals. Forinstance and without limitation physical CAN bus units may transmit asignal from at least a sensor 204 communicatively connected to at leasta pilot control 208. A signal originating from sensor may includeelectrical, electromagnetic, visual, audio, radio waves, or anotherundisclosed signal type alone or in combination. At least a sensor 204communicatively connected to at least a pilot control 208 may include asensor disposed on, near, around or within at least pilot control 208.At least a sensor 204 may include a motion sensor. A “motion sensor”,for the purposes of this disclosure, is a device or component configuredto detect physical movement of an object or grouping of objects. One ofordinary skill in the art would appreciate, after reviewing the entiretyof this disclosure, that motion may include a plurality of typesincluding and not limited to: spinning, rotating, oscillating, gyrating,jumping, sliding, reciprocating, or the like. At least a sensor 204 mayinclude, torque sensor, gyroscope, accelerometer, torque sensor,magnetometer, inertial measurement unit (IMU), pressure sensor, forcesensor, proximity sensor, displacement sensor, vibration sensor, Hallsensor, among others.

Still referring to FIG. 2 , sensor 204 may include a sensor suite whichmay include a plurality of sensors 204 that may detect similar or uniquephenomena. For example, in a non-limiting embodiments, sensor suite mayinclude a plurality of accelerometers, a mixture of accelerometers andgyroscopes, or a mixture of an accelerometer, gyroscope, and torquesensor. The herein disclosed system and method may comprise a pluralityof sensors in the form of individual sensors or a sensor suite workingin tandem or individually. A sensor suite may include a plurality ofindependent sensors, as described herein, where any number of thedescribed sensors may be used to detect any number of physical orelectrical quantities associated with an aircraft power system or anelectrical energy storage system. Independent sensors 204 may includeseparate sensors measuring physical or electrical quantities that may bepowered by and/or in communication with circuits independently, whereeach may signal sensor output to a control circuit such as a usergraphical interface. In an embodiment, use of a plurality of independentsensors may result in redundancy configured to employ more than onesensor that measures the same phenomenon, those sensors being of thesame type, a combination of, or another type of sensor not disclosed, sothat in the event one sensor fails, the ability to detect phenomenon ismaintained and in a non-limiting example, a user alter aircraft usagepursuant to sensor readings. At least a sensor 204 is configured todetect pilot input from at least pilot control 208. At least pilotcontrol 208 may include a throttle lever, inceptor stick, collectivepitch control, steering wheel, brake pedals, pedal controls, toggles,joystick. In some cases, at least a pilot control 208 may include one ormore components as described in documents that are incorporated byreference in this disclosure.

Continuing in reference to FIG. 2 , sensor 204 may be configured toreceive a command datum. A “command datum”, as used in this disclosure,refers an electronic signal representing at least an element of datacorrelated to a desired change in aircraft conditions as described inthe entirety of this disclosure. A command datum may include anycommunication comprising instructions, data interpretable asinstructions, or data readily convertible to instructions for at least aflight component. A command datum may originate from pilot input, in acase of manual flight controls. Alternatively or additionally, in somecases, a command signal may originate from or represent an output froman autonomous function or mode of a flight controller. In some cases, acommand signal may originate from or represent a command from at least aremote device, for example a ground crew system. At least pilot control208 may be communicatively connected to any other component presented insystem, the communicative connection may include redundant connectionsconfigured to safeguard against single-point failure. A signal, such aswithout limitation a command datum, may signal a change to the headingor trim of an electric aircraft. Signal may signal a change to anaircraft's pitch, roll, yaw, or throttle. Command datum, when referringto throttle, may refer to a signal to increase or decrease thrustproduced by at least a propulsor. Command datum may include anelectrical signal. Electrical signals may include analog signals,digital signals, periodic or aperiodic signal, step signals, unitimpulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, and/or pulse width modulated signal,among others. At least a sensor 204 may include circuitry, computingdevices, electronic components, such as CAN, or a combination ofelements, that translates control message 136 into at least anelectronic signal command datum configured to be control an electroniccomponent.

Referring now to FIG. 3 , a non-limiting exemplary embodiment 300 of aCAN bus architecture and resultant transmission signal is illustrated.The CAN bus may include a balanced (differential) 2-wire interfacerunning over either a Shielded Twisted Pair (STP), Un-shielded TwistedPair (UTP), or Ribbon cable. Each node may use a male 9-pin D connector.The CAN protocol, which may perform on physical CAN bus including a CPU,controller, and/or transceiver, may use Non-Return-to-Zero, or NRZ, bitcoding for signal transmission. This means that the signal is constantfor one whole bit time and only one time segment is needed to representone bit. The two bus conductors may be simply referred to as “CAN H” and“CAN L”, although the conductors may be driven differentially inbalanced mode, the levels are shifted, resulting in a waveform thatdiffers. NRZ encoding (with bit-stuffing) for data communication mayrely on a differential two wire bus. The use of NRZ encoding ensurescompact messages with a minimum number of transitions and highresilience to external disturbance.

Referring now to FIG. 4 , a non-limiting exemplary embodiment 400 ofcontroller area network signal transduction is illustrated. CANsignaling may be represented in 1 and 0 binary sequence wherein thelogic refers to 1 (recessive) where no signal is sent (logic 0 wins).For instance, transceiver output at CAN L may float upwards from 1.5 Vto 2.5V, and transceiver output at CAN H may float downwards from 3.5 Vto 2.5V; in other words, there may be no voltage difference, and/or anegligible voltage difference, between CAN L and CAN H. In such anexample, the voltage between the two CAN L and CAN H centers at 2.5 V,which may correspond to either a ‘0’ binary value (bit), oralternatively a ‘1’ binary value. Logic 0 (dominant) may force bus to azero level, for instance, transceiver output at CAN L may be driven backto 1.5V (or kept at a nominal 1.5 V value), and transceiver output atCAN H may be driven back to 3.5V (or kept at a nominal 3.5 V value)(i.e. there is a 2V voltage difference). Voltage may be read, collected,and/or measured at time intervals of 0.1 microseconds (μs), whereinvalue relates to the logic bit (0 or 1) that results from each 0.1 μsperiod. As shown in FIG. 4 , an example waveform showing transmission ofthe sequence {001101} is illustrated. Vertical axis is volts, horizontalaxis is microseconds. Alternatively or additionally, a logic level, orfinite number of states a digital signal can inhabit, may be representedby any difference in voltage between a signal and a ground. For example,CAN L may be kept at a ground state of 0 V and CAN H may be kept at anominal 0 V state, where a difference in voltage between the two equals0 (i.e. no deviation from ground state) and a binary value istransmitted as ‘0’. Correspondingly, if CAN H voltage rises above 0 V,for instance to 1.5 V, (i.e. a difference in voltage between the two isdetected), then the binary value may be transmitted as ‘1’. Differencesin voltage may be sampled at any suitable time point, such asmicrosecond time scale as depicted in FIG. 4 .

Referring now to FIG. 5 , an exemplary embodiment of an aircraft 500 isillustrated. Aircraft 500 may include an electrically powered aircraft.In some embodiments, electrically powered aircraft may be an electricvertical takeoff and landing (eVTOL) aircraft. Electric aircraft may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof “Rotor-basedflight,” as described in this disclosure, is where the aircraftgenerated lift and propulsion by way of one or more powered rotorscoupled with an engine, such as a quadcopter, multi-rotor helicopter, orother vehicle that maintains its lift primarily using downward thrustingpropulsors. “Fixed-wing flight,” as described in this disclosure, iswhere the aircraft is capable of flight using wings and/or foils thatgenerate lift caused by the aircraft's forward airspeed and the shape ofthe wings and/or foils, such as airplane-style flight. At least anaircraft component may include any element of aircraft 500, includingwithout limitation fuselage 504, actuator 508, pilot control 512, sensor516, flight controller 520, and motor 524.

Still referring to FIG. 5 , aircraft 500 may include a fuselage 504. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 504 may comprisestructural elements that physically support the shape and structure ofan aircraft. Structural elements may take a plurality of forms, alone orin combination with other types. Structural elements may vary dependingon the construction type of aircraft and specifically, the fuselage.Fuselage 504 may comprise a truss structure. A truss structure may beused with a lightweight aircraft and may include welded aluminum tubetrusses. A truss, as used herein, is an assembly of beams that create arigid structure, often in combinations of triangles to createthree-dimensional shapes. A truss structure may alternatively comprisetitanium construction in place of aluminum tubes, or a combinationthereof. In some embodiments, structural elements may comprise aluminumtubes and/or titanium beams. In an embodiment, and without limitation,structural elements may include an aircraft skin. Aircraft skin may belayered over the body shape constructed by trusses. Aircraft skin maycomprise a plurality of materials such as aluminum, fiberglass, and/orcarbon fiber, the latter of which will be addressed in greater detaillater in this paper.

Still referring to FIG. 5 , aircraft 500 may include a plurality ofactuators 508. Actuator 508 may include any actuator described in thisdisclosure, for instance in reference to FIGS. 1-4 and 6-7 . In anembodiment, actuator 508 may be mechanically coupled to an aircraft. Asused herein, a person of ordinary skill in the art would understand“mechanically coupled” to mean that at least a portion of a device,component, or circuit is connected to at least a portion of the aircraftvia a mechanical coupling. Said mechanical coupling can include, forexample, rigid coupling, such as beam coupling, bellows coupling, bushedpin coupling, constant velocity, split-muff coupling, diaphragmcoupling, disc coupling, donut coupling, elastic coupling, flexiblecoupling, fluid coupling, gear coupling, grid coupling, Hirth joints,hydrodynamic coupling, jaw coupling, magnetic coupling, Oldham coupling,sleeve coupling, tapered shaft lock, twin spring coupling, rag jointcoupling, universal joints, or any combination thereof. As used in thisdisclosure an “aircraft” is vehicle that may fly. As a non-limitingexample, aircraft may include airplanes, helicopters, airships, blimps,gliders, paramotors, and the like thereof. In an embodiment, mechanicalcoupling may be used to connect the ends of adjacent parts and/orobjects of an electric aircraft. Further, in an embodiment, mechanicalcoupling may be used to join two pieces of rotating electric aircraftcomponents.

With continued reference to FIG. 5 , a plurality of actuators 508 may beconfigured to produce a torque. As used in this disclosure a “torque” isa measure of force that causes an object to rotate about an axis in adirection. For example, and without limitation, torque may rotate anaileron and/or rudder to generate a force that may adjust and/or affectaltitude, airspeed velocity, groundspeed velocity, direction duringflight, and/or thrust. For example, plurality of actuators 508 mayinclude a component used to produce a torque that affects aircrafts'roll and pitch, such as without limitation one or more ailerons. An“aileron,” as used in this disclosure, is a hinged surface which formpart of the trailing edge of a wing in a fixed wing aircraft, and whichmay be moved via mechanical means such as without limitationservomotors, mechanical linkages, or the like. As a further example,plurality of actuators 508 may include a rudder, which may include,without limitation, a segmented rudder that produces a torque about avertical axis. Additionally or alternatively, plurality of actuators 508may include other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjustmovement of aircraft 500. Plurality of actuators 508 may include one ormore rotors, turbines, ducted fans, paddle wheels, and/or othercomponents configured to propel a vehicle through a fluid mediumincluding, but not limited to air.

Still referring to FIG. 5 , plurality of actuators 508 may include atleast a propulsor component. As used in this disclosure a “propulsorcomponent” is a component and/or device used to propel a craft byexerting force on a fluid medium, which may include a gaseous mediumsuch as air or a liquid medium such as water. In an embodiment, when apropulsor twists and pulls air behind it, it may, at the same time, pushan aircraft forward with an amount of force and/or thrust. More airpulled behind an aircraft results in greater thrust with which theaircraft is pushed forward. Propulsor component may include any deviceor component that consumes electrical power on demand to propel anelectric aircraft in a direction or other vehicle while on ground orin-flight. In an embodiment, propulsor component may include a pullercomponent. As used in this disclosure a “puller component” is acomponent that pulls and/or tows an aircraft through a medium. As anon-limiting example, puller component may include a flight componentsuch as a puller propeller, a puller motor, a puller propulsor, and thelike. Additionally, or alternatively, puller component may include aplurality of puller flight components. In another embodiment, propulsorcomponent may include a pusher component. As used in this disclosure a“pusher component” is a component that pushes and/or thrusts an aircraftthrough a medium. As a non-limiting example, pusher component mayinclude a pusher component such as a pusher propeller, a pusher motor, apusher propulsor, and the like. Additionally, or alternatively, pusherflight component may include a plurality of pusher flight components.

In another embodiment, and still referring to FIG. 5 , propulsor mayinclude a propeller, a blade, or any combination of the two. A propellermay function to convert rotary motion from an engine or other powersource into a swirling slipstream which may push the propeller forwardsor backwards. Propulsor may include a rotating power-driven hub, towhich several radial airfoil-section blades may be attached, such thatan entire whole assembly rotates about a longitudinal axis. As anon-limiting example, blade pitch of propellers may be fixed at a fixedangle, manually variable to a few set positions, automatically variable(e.g. a “constant-speed” type), and/or any combination thereof asdescribed further in this disclosure. As used in this disclosure a“fixed angle” is an angle that is secured and/or substantially unmovablefrom an attachment point. For example, and without limitation, a fixedangle may be an angle of 2.2° inward and/or 1.7° forward. As a furthernon-limiting example, a fixed angle may be an angle of 3.6° outwardand/or 2.7° backward. In an embodiment, propellers for an aircraft maybe designed to be fixed to their hub at an angle similar to the threadon a screw makes an angle to the shaft; this angle may be referred to asa pitch or pitch angle which may determine a speed of forward movementas the blade rotates. Additionally or alternatively, propulsor componentmay be configured having a variable pitch angle. As used in thisdisclosure a “variable pitch angle” is an angle that may be moved and/orrotated. For example, and without limitation, propulsor component may beangled at a first angle of 3.3° inward, wherein propulsor component maybe rotated and/or shifted to a second angle of 1.7° outward.

Still referring to FIG. 5 , propulsor may include a thrust element whichmay be integrated into the propulsor. Thrust element may include,without limitation, a device using moving or rotating foils, such as oneor more rotors, an airscrew or propeller, a set of airscrews orpropellers such as contra-rotating propellers, a moving or flappingwing, or the like. Further, a thrust element, for example, can includewithout limitation a marine propeller or screw, an impeller, a turbine,a pump-jet, a paddle or paddle-based device, or the like.

With continued reference to FIG. 5 , plurality of actuators 508 mayinclude power sources, control links to one or more elements, fuses,and/or mechanical couplings used to drive and/or control any otherflight component. Plurality of actuators 508 may include a motor thatoperates to move one or more flight control components and/or one ormore control surfaces, to drive one or more propulsors, or the like. Amotor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof.Alternatively or additionally, a motor may be driven by an inverter. Amotor may also include electronic speed controllers, inverters, or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking.

Still referring to FIG. 5 , plurality of actuators 508 may include anenergy source. An energy source may include, for example, a generator, aphotovoltaic device, a fuel cell such as a hydrogen fuel cell, directmethanol fuel cell, and/or solid oxide fuel cell, an electric energystorage device (e.g. a capacitor, an inductor, and/or a battery). Anenergy source may also include a battery cell, or a plurality of batterycells connected in series into a module and each module connected inseries or in parallel with other modules. Configuration of an energysource containing connected modules may be designed to meet an energy orpower requirement and may be designed to fit within a designatedfootprint in an electric aircraft in which system may be incorporated.

In an embodiment, and still referring to FIG. 5 , an energy source maybe used to provide a steady supply of electrical power to a load over aflight by an electric aircraft 500. For example, energy source may becapable of providing sufficient power for “cruising” and otherrelatively low-energy phases of flight. An energy source may also becapable of providing electrical power for some higher-power phases offlight as well, particularly when the energy source is at a high SOC, asmay be the case for instance during takeoff. In an embodiment, energysource may include an emergency power unit which may be capable ofproviding sufficient electrical power for auxiliary loads includingwithout limitation, lighting, navigation, communications, de-icing,steering or other systems requiring power or energy. Further, energysource may be capable of providing sufficient power for controlleddescent and landing protocols, including, without limitation, hoveringdescent or runway landing. As used herein the energy source may havehigh power density where electrical power an energy source can usefullyproduce per unit of volume and/or mass is relatively high. As used inthis disclosure, “electrical power” is a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, forinstance at an expense of maximal total specific energy density or powercapacity. Non-limiting examples of items that may be used as at least anenergy source include batteries used for starting applications includingLi ion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 5 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Modulemay include batteries connected in parallel or in series or a pluralityof modules connected either in series or in parallel designed to satisfyboth power and energy requirements. Connecting batteries in series mayincrease a potential of at least an energy source which may provide morepower on demand. High potential batteries may require cell matching whenhigh peak load is needed. As more cells are connected in strings, theremay exist a possibility of one cell failing which may increaseresistance in module and reduce overall power output as voltage of themodule may decrease as a result of that failing cell. Connectingbatteries in parallel may increase total current capacity by decreasingtotal resistance, and it also may increase overall amp-hour capacity.Overall energy and power outputs of at least an energy source may bebased on individual battery cell performance or an extrapolation basedon a measurement of at least an electrical parameter. In an embodimentwhere energy source includes a plurality of battery cells, overall poweroutput capacity may be dependent on electrical parameters of eachindividual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to a weakest cell. Energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source. Exemplary energy sources are disclosed in detail inU.S. patent application Ser. Nos. 16/948,157 and 16/048,140 bothentitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” byS. Donovan et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 5 , according to some embodiments, an energysource may include an emergency power unit (EPU) (i.e., auxiliary powerunit). As used in this disclosure an “emergency power unit” is an energysource as described herein that is configured to power an essentialsystem for a critical function in an emergency, for instance withoutlimitation when another energy source has failed, is depleted, or isotherwise unavailable. Exemplary non-limiting essential systems includenavigation systems, such as MFD, GPS, VOR receiver or directional gyro,and other essential flight components, such as propulsors.

Still referring to FIG. 5 , another exemplary actuator may includelanding gear. Landing gear may be used for take-off and/orlanding/Landing gear may be used to contact ground while aircraft 500 isnot in flight. Exemplary landing gear is disclosed in detail in U.S.patent application Ser. No. 17/196,719 entitled “SYSTEM FOR ROLLINGLANDING GEAR” by R. Griffin et al., which is incorporated in itsentirety herein by reference.

Still referring to FIG. 5 , aircraft 500 may include a pilot control552, including without limitation, a hover control, a thrust control, aninceptor stick, a cyclic, and/or a collective control. As used in thisdisclosure a “collective control” is a mechanical control of an aircraftthat allows a pilot to adjust and/or control the pitch angle of theplurality of actuators 508. For example and without limitation,collective control may alter and/or adjust the pitch angle of all of themain rotor blades collectively. For example, and without limitationpilot control 512 may include a yoke control. As used in this disclosurea “yoke control” is a mechanical control of an aircraft to control thepitch and/or roll. For example and without limitation, yoke control mayalter and/or adjust the roll angle of aircraft 500 as a function ofcontrolling and/or maneuvering ailerons. In an embodiment, pilot control512 may include one or more foot-brakes, control sticks, pedals,throttle levels, and the like thereof. In another embodiment, andwithout limitation, pilot control 512 may be configured to control aprincipal axis of the aircraft. As used in this disclosure a “principalaxis” is an axis in a body representing one three dimensionalorientations. For example, and without limitation, principal axis ormore yaw, pitch, and/or roll axis. Principal axis may include a yawaxis. As used in this disclosure a “yaw axis” is an axis that isdirected towards the bottom of the aircraft, perpendicular to the wings.For example, and without limitation, a positive yawing motion mayinclude adjusting and/or shifting the nose of aircraft 500 to the right.Principal axis may include a pitch axis. As used in this disclosure a“pitch axis” is an axis that is directed towards the right laterallyextending wing of the aircraft. For example, and without limitation, apositive pitching motion may include adjusting and/or shifting the noseof aircraft 500 upwards. Principal axis may include a roll axis. As usedin this disclosure a “roll axis” is an axis that is directedlongitudinally towards the nose of the aircraft, parallel to thefuselage. For example, and without limitation, a positive rolling motionmay include lifting the left and lowering the right wing concurrently.

Still referring to FIG. 5 , pilot control 512 may be configured tomodify a variable pitch angle. For example, and without limitation,pilot control 512 may adjust one or more angles of attack of apropeller. As used in this disclosure an “angle of attack” is an anglebetween the chord of the propeller and the relative wind. For example,and without limitation angle of attack may include a propeller bladeangled 3.2°. In an embodiment, pilot control 512 may modify the variablepitch angle from a first angle of 2.71° to a second angle of 3.82°.Additionally or alternatively, pilot control 512 may be configured totranslate a pilot desired torque for flight component 108. For example,and without limitation, pilot control 512 may translate that a pilot'sdesired torque for a propeller be 160 lb. ft. of torque. As a furthernon-limiting example, pilot control 512 may introduce a pilot's desiredtorque for a propulsor to be 290 lb. ft. of torque. Additionaldisclosure related to pilot control 512 may be found in U.S. patentapplication Ser. Nos. 17/001,845 and 16/929,206 both of which areentitled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT” byC. Spiegel et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 5 , aircraft 500 may include a loading system. Aloading system may include a system configured to load an aircraft ofeither cargo or personnel. For instance, some exemplary loading systemsmay include a swing nose, which is configured to swing the nose ofaircraft 500 of the way thereby allowing direct access to a cargo baylocated behind the nose. A notable exemplary swing nose aircraft isBoeing 747. Additional disclosure related to loading systems can befound in U.S. patent application Ser. No. 17/137,594 entitled “SYSTEMAND METHOD FOR LOADING AND SECURING PAYLOAD IN AN AIRCRAFT” by R.Griffin et al., entirety of which in incorporated herein by reference.

Still referring to FIG. 5 , aircraft 500 may include a sensor 516.Sensor 516 may include any sensor described in this disclosure, forinstance in reference to FIGS. 1-4 and FIGS. 6-7 . Sensor 516 may beconfigured to sense a characteristic of an aircraft, an aircraftcomponent, an environment, a pilot, or any phenomenon associated withaircraft 500. Sensor may be a device, module, and/or subsystem,utilizing any hardware, software, and/or any combination thereof tosense a characteristic and/or changes thereof, in an instantenvironment, for instance without limitation an aircraft component,which the sensor is proximal to or otherwise in a sensed communicationwith, and transmit information associated with the characteristic, forinstance without limitation digitized data. Sensor 516 may bemechanically and/or communicatively coupled to aircraft 500, including,for instance, to at least a pilot control 512. Sensor 516 may beconfigured to sense a characteristic associated with at least a pilotcontrol 512. An environmental sensor may include without limitation oneor more sensors used to detect ambient temperature, barometric pressure,and/or air velocity, one or more motion sensors which may includewithout limitation gyroscopes, accelerometers, inertial measurement unit(IMU), and/or magnetic sensors, one or more humidity sensors, one ormore oxygen sensors, or the like. Additionally or alternatively, sensor516 may include at least a geospatial sensor. Sensor 516 may be locatedinside an aircraft; and/or be included in and/or attached to at least aportion of the aircraft. Sensor may include one or more proximitysensors, displacement sensors, vibration sensors, and the like thereof.Sensor may be used to monitor the status of aircraft 500 for bothcritical and non-critical functions. Sensor may be incorporated intovehicle or aircraft or be remote.

Still referring to FIG. 5 , in some embodiments, sensor 516 may beconfigured to sense a characteristic associated with any aircraftcomponent described in this disclosure. Non-limiting examples of asensor 516 may include an inertial measurement unit (IMU), anaccelerometer, a gyroscope, a proximity sensor, a pressure sensor, alight sensor, a pitot tube, an air speed sensor, a position sensor, aspeed sensor, a switch, a thermometer, a strain gauge, an acousticsensor, and an electrical sensor. In some cases, sensor 516 may sense acharacteristic as an analog measurement, for instance, yielding acontinuously variable electrical potential indicative of the sensedcharacteristic. In these cases, sensor 516 may additionally comprise ananalog to digital converter (ADC) as well as any additionally circuitry,such as without limitation a Whetstone bridge, an amplifier, a filter,and the like. For instance, in some cases, sensor 516 may comprise astrain gage configured to determine loading of one or flight components,for instance landing gear. Strain gage may be included within a circuitcomprising a Whetstone bridge, an amplified, and a bandpass filter toprovide an analog strain measurement signal having a high signal tonoise ratio, which characterizes strain on a landing gear member. An ADCmay then digitize analog signal produces a digital signal that can thenbe transmitted other systems within aircraft 500, for instance withoutlimitation a computing system, a pilot display, and a memory component.Alternatively or additionally, sensor 516 may sense a characteristic ofa pilot control 512 digitally. For instance in some embodiments, sensor516 may sense a characteristic through a digital means or digitize asensed signal natively. In some cases, for example, sensor 516 mayinclude a rotational encoder and be configured to sense a rotationalposition of a pilot control; in this case, the rotational encoderdigitally may sense rotational “clicks” by any known method, such aswithout limitation magnetically, optically, and the like.

Still referring to FIG. 5 , electric aircraft 500 may include at least amotor 524, which may be mounted on a structural feature of the aircraft.Design of motor 524 may enable it to be installed external to structuralmember (such as a boom, nacelle, or fuselage) for easy maintenanceaccess and to minimize accessibility requirements for the structure;this may improve structural efficiency by requiring fewer large holes inthe mounting area. In some embodiments, motor 524 may include two mainholes in top and bottom of mounting area to access bearing cartridge.Further, a structural feature may include a component of electricaircraft 500. For example, and without limitation structural feature maybe any portion of a vehicle incorporating motor 524, including anyvehicle as described in this disclosure. As a further non-limitingexample, a structural feature may include without limitation a wing, aspar, an outrigger, a fuselage, or any portion thereof; persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of many possible features that may function as at least astructural feature. At least a structural feature may be constructed ofany suitable material or combination of materials, including withoutlimitation metal such as aluminum, titanium, steel, or the like, polymermaterials or composites, fiberglass, carbon fiber, wood, or any othersuitable material. As a non-limiting example, at least a structuralfeature may be constructed from additively manufactured polymer materialwith a carbon fiber exterior; aluminum parts or other elements may beenclosed for structural strength, or for purposes of supporting, forinstance, vibration, torque or shear stresses imposed by at leastpropulsor 508. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various materials, combinations ofmaterials, and/or constructions techniques.

Still referring to FIG. 5 , electric aircraft 500 may include a verticaltakeoff and landing aircraft (eVTOL). As used herein, a “verticaltake-off and landing (VTOL) aircraft” is one that can hover, take off,and land vertically. An “eVTOL,” as used herein, is an electricallypowered aircraft typically using an energy source, of a plurality ofenergy sources to power the aircraft. In order to optimize the power andenergy necessary to propel the aircraft. eVTOL may be capable ofrotor-based cruising flight, rotor-based takeoff, rotor-based landing,fixed-wing cruising flight, airplane-style takeoff, airplane-stylelanding, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generated lift and propulsion byway of one or more powered rotors coupled with an engine, such as a“quad copter,” multi-rotor helicopter, or other vehicle that maintainsits lift primarily using downward thrusting propulsors. Fixed-wingflight, as described herein, is where the aircraft is capable of flightusing wings and/or foils that generate life caused by the aircraft'sforward airspeed and the shape of the wings and/or foils, such asairplane-style flight.

With continued reference to FIG. 5 , a number of aerodynamic forces mayact upon the electric aircraft 500 during flight. Forces acting onelectric aircraft 500 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 500 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 500 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 500 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 500 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 500 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 500 downward due to the force of gravity. Anadditional force acting on electric aircraft 500 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor 508 of the electric aircraft.Lift generated by the airfoil may depend on speed of airflow, density ofair, total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 500 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of electric aircraft 500,including without limitation propulsors and/or propulsion assemblies. Inan embodiment, motor 524 may eliminate need for many external structuralfeatures that otherwise might be needed to join one component to anothercomponent. Motor 524 may also increase energy efficiency by enabling alower physical propulsor profile, reducing drag and/or wind resistance.This may also increase durability by lessening the extent to which dragand/or wind resistance add to forces acting on electric aircraft 500and/or propulsors.

Referring to FIG. 6 , an exemplary digital twin 600 is schematicallyillustrated. digital twin 600 may include a computing device 604. Asused in this disclosure, a “digital twin” is an up-to-date virtualrepresentation of a physical component or process, for instance andwithout limitation an aircraft such as an eVTOL aircraft. Digital twin600 may represent an aircraft 608. In some cases, aircraft 608 mayinclude an individual aircraft. Alternatively or additionally, in somecases, digital twin 608 may represent a class, type, lot, or aggregateof aircraft 608. In some cases, digital twin 608 may represent anaircraft which is incomplete, or in a pre-production design stage. Insome cases, digital twin 608 may represent aircraft 608 which are inuse. In some cases, a digital twin may include any of a digital twinprototype (DTP), a digital twin instance (DTI), and a digital twinaggregate (DTA). DTP consists of designs, analyses, and processes torealize a physical component. In some cases, a DTP exists before thereis a physical component. DTI is digital twin of any of an individualinstance of component, for instance after the component has beenmanufactured. DTA is an aggregation of DTIs whose data and informationcan be used for interrogation about a physical component, prognostics,and learning. In some embodiments, specific information contained indigital twin may be driven by use cases. Digital twin may be a logicalconstruct, meaning that actual data and information constituting thedigital twin may be contained in any number of other computer devicesand/or software. A digital twin 600 may include an integratedmulti-physics, multiscale, probabilistic simulation of an as-builtvehicle or system that uses best available physical models, sensorupdates, fleet history, and the like, to mirror an eVTOL or at least anaircraft component. In some cases, digital twin 600 digital twin may bea virtual instance of an aircraft 608 (twin) that is continually updatedwith the aircraft's performance, maintenance, and health status data,for example throughout the aircraft's life cycle.

With continued reference to FIG. 6 , in some cases, digital twin 600 mayinclude a at least a network 612 communicatively connecting at least anaircraft component of aircraft 608 and computing device 604. At least anetwork 612 may include any network described in this disclosure,including without limitation an avionic mesh network. Computing device604 may likewise include any computing device described in thisdisclosure. Digital twin 600 may include any number of models,simulations, digital representations, and the like. Computing device 604may access, process and/or store some or all models constituting digitaltwin 600.

With continued reference to FIG. 6 , in some embodiments digital twin600 may include a mechanical model 616 of aircraft 608. Mechanical model616 may include, for example without limitation, computer-aided design(CAD) models, 3D models, 2D models, material models, finite elementanalysis (FEA) models, manufacturing models, Stress/vibrational/spectralanalysis models, and the like.

With continued reference to FIG. 6 , in some embodiments digital twin600 may include a flight controller model 620. Flight controller model620 may include a logical model of any controller, processor, orcomputing device operative on or in service to function of aircraft 608.For example, flight controller model 620 may model any flight controllersystem or subsystem described in this disclosure. In some cases,modeling of an integrated circuit of flight controller may includehardware emulation. For example, an integrated circuit may be modeled byan emulator configured to emulate an integrated circuit, controllerhardware/firmware, FPGA gate arrangement, and the like. In some cases,electronic design automation (EDA) may be used to model at least aportion of flight controller model 620. Additionally non-limitingexemplary electronic circuit simulation and modeling methods includetransistor simulation, logic simulation, behavioral simulation,technology CAD, electromagnetic field solvers, and the like. In somecases, flight controller model 620 may be configured to allow forfunctional verification of at least a portion of a flight controller. Insome cases, digital twin 600 may model at least a portion and/or allcomputing devices within or utilized by an aircraft 608. electricalvertical take-off and landing vehicles may require many computingdevices, not only in-flight controllers, but also for function of otherflight components.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include flight component model 624. In some cases, flightcomponent model 624 may be configured to model at least a flightcomponent associated with aircraft. Flight component may include anyflight component described in this disclosure.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include a battery model 628. Battery model 628 may include anymodel related to at least property, characteristic, or function of abattery located within aircraft. In some cases, battery model 628 mayinclude a model of a battery controller, management, and/or monitoringsystem. Disclosure related to battery management for eVTOL aircraft maybe found in patent application Ser. No. 17/108,798 and Ser. No.17,111,002, entitled “PACK LEVEL BATTERY MANAGEMENT SYSTEM” and“ELECTRICAL DISTRIBUTION MONITORING SYSTEM FOR AN ELECTRIC AIRCRAFT,”respectively, each of which is incorporated herein by reference in itsentirety. In some cases, a battery model 628 may include anelectrochemical model of battery, which may be predictive of energyefficiencies and heat generation and transfer of at least a battery. Insome cases, a battery model 628 may be configured to predict batterylifetime, given known battery parameters, for example measured batteryperformance, temperature, utilization, and the like.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include a fluid model 632. A fluid model 632 may model fluidswithin and/or interacting with aircraft, for example environmental air.In some cases, fluid model may include computation fluid dynamicmodeling. Exemplary computation fluid dynamics modeling softwareincludes Ansys Fluids from Ansys of Canonsburg, Pennsylvania, U.S.A.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include a manufacturing model 636. In some cases, amanufacturing model may include representations of an actualmanufacturing process for example for an individual aircraft 608. Insome cases, manufacturing model 636 may include reference to an aircrafthistory file, manufacturing records, and/or maintenance records. Forexample, in some cases, deviations for a standard manufacturing and/ormaintenance process may be included in a manufacturing model 636. Insome case, a manufacturing model may include information related totraceability of at least an aircraft component, for examplemanufacturer, model, and lot number for given critical aircraftcomponents.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include a sensor model 640. A sensor model 640 may model atleast a sensor of aircraft 608. Sensor may include any sensor describedin this disclosure. In some cases, sensor model 640 may emulate and/orsimulate expected measurements for at least a sensor on aircraft 608.Alternatively and/or additionally in some embodiments, sensor model 640may be informed by actual measurements communicated at least a sensor ofaircraft 608. In some cases, a difference between an expectedmeasurement and an actual measurement may be found, for example bysensor model 640; the difference may be used to improve digital twin 600performance, for example trough data-based model updating and/orrecalibration. In some cases, sensor model 640 may be used to sense,detect, or otherwise measure performance of any other model of digitaltwin 600 or aircraft 608.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include a thermal model 644. Exemplary thermal modeling softwareincludes without limitation COMSOL from COMSOL, Inc. of Burlington,Massachusetts, U.S.A. Thermal modeling may include analytical model, forexample finite-elemental analysis based upon Fourier conduction,Newton's law of Cooling, and/or Kirchhoff's law of thermal radiation.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may include a multi-physics model 648. A multi-physics model 648 mayinclude analytical models of any well-known or otherwise predictablephysical phenomenon. In some cases, a multi-physics model 648 mayinclude elements or portions of any other model described in thisdisclosure. A multi-physics model 648 may, for example, includeelectromagnetic radiation models. A multi-physics model may predictoptical and/or electrical performance of one or more aircraftcomponents. Multi-physics modeling may include coupled processes orsystems involving more than one simultaneously occurring physical field.A multi-physics model may include many physical models. A multi-physicsmodel may include partial differential equations and/or tensor analysis.A multi-physics model may include any model of a physical process, suchas without limitation heat transfer (thermo-), pore water movement(hydro-), concentration field (concentro or diffuso/convecto/advecto),stress and strain (mechano-), dynamics (dyno-), chemical reactions(chemo- or chemico-), electrostatics (electro-), neutronics (neutro-),magnetostatics (magneto-), and the like.

With continued reference to FIG. 6 , in some embodiments, digital twin600 may model, simulate, predict, and/or determine an aspect of aircraft608 using machine-learning processes, including any machine-learningprocess described in this application. Digital twin 600 may includeanalytical models, for example those based upon known physical laws andphenomena, such as Newton's laws of motion. Alternatively and/oradditionally, digital twin 600 may include data-driven models basedlargely on observed data, for example Monte-Carlo modeling and/ormachine-learning processes. In some cases, digital twin 600 may beconstituted of digital threads. According to some embodiments, a digitalthread may be considered a lowest level design and specification for adigital representation of a physical item. Use of digital threads may,in some cases, ensure deep coherence between models of a digital twin600. In some cases, a digital twin 600 may include a design equationand/or design matrix. A design equation may mathematically representsome or all design requirements and parameters associated with aparticular design, for example an aircraft 608.

Referring to FIG. 7 , an avionic mesh network 700 is schematicallyillustrated. According to some embodiments, an avionic mesh network mayinclude a single network. Alternatively or additionally, an avionic meshnetwork may include more than a single network. A single networks may bedifferentiated according to address, for example Internet Protocoladdress, gateway, or name server used. For example, in some cases,multiple networks may use different gateways, even though the multiplenetworks may still be within communicative connection with one another.

With continued reference to FIG. 7 , in some embodiments, an avionicmesh network 700 may include inter-aircraft network nodes,intra-aircraft network nodes, as well as non-aircraft network nodes. Asused in this disclosure, a “network node” is any componentcommunicatively coupled to at least a network. For example, a networknode may include an endpoint, for example a computing device on network,a switch, a router, a bridge, and the like. A network node may include aredistribution point, for example a switch, or an endpoint, for examplea component communicatively connected to network. As used in thisdisclosure, “inter-aircraft network nodes” are two or more network nodesthat are physically located in two or more aircraft and communicativelyconnected by way of an inter-aircraft network. As used in thisdisclosure, “intra-aircraft network nodes” are two or moreintra-aircraft network nodes that are each physically located within asingle aircraft and communicatively connected. As used in thisdisclosure, a “non-aircraft network node” is a network node that is notlocated on an aircraft and is communicatively connected to a network.

With continued reference to FIG. 7 , in some embodiments, avionic meshnetwork 700 may include a wireless mesh network organized in a meshtopology. A mesh topology may include a networked infrastructure inwhich network nodes may be connected directly, dynamically, and/ornon-hierarchically to many other nodes (e.g., as many other nodes aspossible). In some cases, a mesh topology may be facilitate cooperationbetween network nodes, for example redistributive network nodes, inrouting of communication between network participants (e.g., othernetwork nodes). A mesh topology may facilitate a lack of dependency onany given node, thereby allowing other nodes to participate in relayingcommunication. In some cases, mesh networks may dynamicallyself-organize and self-configure. Self-configuration enables dynamicdistribution of workloads, particularly in event a network node failure,thereby contributing to fault-tolerance and reduced maintenancerequirements. In some embodiments, mesh networks can relay messagesusing either a flooding technique or a routing technique. A floodingtechnique sends a message to every network node, flooding network withthe message. A routing technique allows a mesh network to communicate amessage is propagated along a determined nodal path to the message'sintended destination. Message routing may be performed by mesh networksin part by ensuring that all nodal paths are available. Nodal pathavailability may be ensured by maintaining continuous nodal networkconnections and reconfiguring nodal paths with an occurrence of brokennodal paths. Reconfiguration of nodal paths, in some cases, may beperformed by utilizing self-healing algorithms, such as withoutlimitation Shortest Path Bridging. Self-healing allows a routing-basednetwork to operate when a node fails or when a connection becomesunreliable. In some embodiments, a mesh network having all network nodesconnected to each other may be termed a fully connected network. Fullyconnected wired networks have advantages of security and reliability.For example, an unreliable wired connection between two wired networknodes will only affect only two nodes attached to the unreliable wiredconnection.

With continued reference to FIG. 7 , an exemplary avionic mesh network700 is shown providing communicative connection between a computingdevice 704 and aircraft 708A-C. Computing device 704 may include anycomputing device described in this disclosure. In some embodiments,computing device 704 may be connected to a terrestrial network 712.Terrestrial networks 712 may include any network described in thisdisclosure and may include, without limitation, wireless networks, localarea networks (LANs), wide area networks (WANs), ethernet, Internet,mobile broadband, fiber optic communication, and the like. In somecases, a grounded aircraft 708C may be connected to an avionic meshnetwork 700 by way of a terrestrial network 712. In some cases, avionicmesh network 700 may include a wireless communication node 716. Awireless communication node 716 may provide communicative connection byway of wireless networking. Wireless networking may include any wirelessnetwork method described in this disclosure, including withoutlimitation Wi-Fi, mobile broadband, optical communication, radiocommunication, and the like. In some cases, wireless communication node716 may be configured to connect with a first airborne aircraft inflight 708A. First airborne aircraft in some embodiments may include atleast a first intra-aircraft network node 720A. As described above,first intra-aircraft network node 720A may be configured to connect toother nodes within first airborne aircraft 708A. In some cases, avionicmesh network 700 may be configured to provide inter-aircraftcommunication, for instance by using a first inter-aircraft network node724A. In some cases, first inter-aircraft network node may be configuredto communicate with a second inter-aircraft network node 724B.Inter-aircraft nodes 720A-B may include radio communication and/oroptical wireless communication, for example free space opticalcommunication.

With continued reference to FIG. 7 , avionic mesh network 700 may beadditionally configured to provide for encrypted and/or securedcommunication between components, i.e., nodes, communicative on thenetwork. In some cases, encrypted communication on network 700 may beprovided for by way of end-to-end encryption. Exemplary non-limitedend-to-end encryption methods include symmetric key encryption,asymmetric key encryption, public key encryption methods, private keyencryption methods and the like. In some cases, avionic mesh network 700and/or another network may be configured to provide secure key exchangefor encryption methods. Exemplary non-limiting key exchange methodsinclude Diffie-Hellman key exchange, Supersingular isogeny key exchange,use of at least a trusted key authority, password authenticated keyagreement, forward secrecy, quantum key exchange, and the like. In somecases, an avionic mesh network 700 may include at least an opticalnetwork component, for example fiber optic cables, wireless opticalnetworks, and/or free space optical network. In some cases, encryptedcommunication between network nodes may be implemented by way of opticalnetwork components. For example, quantum key exchange in someembodiments, may defeat man-in-the-middle attacks. This is generallybecause, observation of a quantum system disturbs the quantum system.Quantum key exchange in some cases, uses this general characteristic ofquantum physics to communicate sensitive information, such as anencryption key, by encoding the sensitive information in polarizationstate of quantum of radiation. At least a polarization sensitivedetector may be used to decode sensitive information.

Still referring to FIG. 7 , in an embodiment, methods and systemsdescribed herein may perform or implement one or more aspects of acryptographic system. In one embodiment, a cryptographic system is asystem that converts data from a first form, known as “plaintext,” whichis intelligible when viewed in its intended format, into a second form,known as “ciphertext,” which is not intelligible when viewed in the sameway. Ciphertext may be unintelligible in any format unless firstconverted back to plaintext. In one embodiment, a process of convertingplaintext into ciphertext is known as “encryption.” Encryption processmay involve the use of a datum, known as an “encryption key,” to alterplaintext. Cryptographic system may also convert ciphertext back intoplaintext, which is a process known as “decryption.” Decryption processmay involve the use of a datum, known as a “decryption key,” to returnthe ciphertext to its original plaintext form. In embodiments ofcryptographic systems that are “symmetric,” decryption key isessentially the same as encryption key: possession of either key makesit possible to deduce the other key quickly without further secretknowledge. Encryption and decryption keys in symmetric cryptographicsystems may be kept secret and shared only with persons or entities thatthe user of the cryptographic system wishes to be able to decrypt theciphertext. One example of a symmetric cryptographic system is theAdvanced Encryption Standard (“AES”), which arranges plaintext intomatrices and then modifies the matrices through repeated permutationsand arithmetic operations with an encryption key.

Still referring to FIG. 7 , in embodiments of cryptographic systems thatare “asymmetric,” either encryption or decryption key cannot be readilydeduced without additional secret knowledge, even given the possessionof a corresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, and a definition for addition where A+B=−R,the point where a line connecting point A and point B intersects theelliptic curve, where “0,” the identity, is a point at infinity in aprojective plane containing the elliptic curve, finding a number k suchthat adding P to itself k times results in Q is computationallyimpractical, given correctly selected elliptic curve, finite field, andP and Q.

With continued reference to FIG. 7 , in some cases, avionic mesh network700 may be configured to allow message authentication between networknodes. In some cases, message authentication may include a property thata message has not been modified while in transit and that receivingparty can verify source of the message. In some embodiments, messageauthentication may include us of message authentication codes (MACs),authenticated encryption (AE), and/or digital signature. Messageauthentication code, also known as digital authenticator, may be used asan integrity check based on a secret key shared by two parties toauthenticate information transmitted between them. In some cases, adigital authenticator may use a cryptographic hash and/or an encryptionalgorithm.

Still referring to FIG. 7 , in some embodiments, systems and methodsdescribed herein produce cryptographic hashes, also referred to by theequivalent shorthand term “hashes.” A cryptographic hash, as usedherein, is a mathematical representation of a lot of data, such as filesor blocks in a block chain as described in further detail below; themathematical representation is produced by a lossy “one-way” algorithmknown as a “hashing algorithm.” Hashing algorithm may be a repeatableprocess; that is, identical lots of data may produce identical hasheseach time they are subjected to a particular hashing algorithm. Becausehashing algorithm is a one-way function, it may be impossible toreconstruct a lot of data from a hash produced from the lot of datausing the hashing algorithm. In the case of some hashing algorithms,reconstructing the full lot of data from the corresponding hash using apartial set of data from the full lot of data may be possible only byrepeatedly guessing at the remaining data and repeating the hashingalgorithm; it is thus computationally difficult if not infeasible for asingle computer to produce the lot of data, as the statisticallikelihood of correctly guessing the missing data may be extremely low.However, the statistical likelihood of a computer of a set of computerssimultaneously attempting to guess the missing data within a usefultimeframe may be higher, permitting mining protocols as described infurther detail below.

Still referring to FIG. 7 , in an embodiment, hashing algorithm maydemonstrate an “avalanche effect,” whereby even extremely small changesto lot of data produce drastically different hashes. This may thwartattempts to avoid the computational work necessary to recreate a hash bysimply inserting a fraudulent datum in data lot, enabling the use ofhashing algorithms for “tamper-proofing” data such as data contained inan immutable ledger as described in further detail below. This avalancheor “cascade” effect may be evinced by various hashing processes; personsskilled in the art, upon reading the entirety of this disclosure, willbe aware of various suitable hashing algorithms for purposes describedherein. Verification of a hash corresponding to a lot of data may beperformed by running the lot of data through a hashing algorithm used toproduce the hash. Such verification may be computationally expensive,albeit feasible, potentially adding up to significant processing delayswhere repeated hashing, or hashing of large quantities of data, isrequired, for instance as described in further detail below. Examples ofhashing programs include, without limitation, SHA256, a NIST standard;further current and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Polyl305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2^(n/2)) for n outputbits; thus, it may take on the order of 2²⁵⁶ operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Continuing to refer to FIG. 7 , a “secure proof,” as used in thisdisclosure, is a protocol whereby an output is generated thatdemonstrates possession of a secret, such as device-specific secret,without demonstrating the entirety of the device-specific secret; inother words, a secure proof by itself, is insufficient to reconstructthe entire device-specific secret, enabling the production of at leastanother secure proof using at least a device-specific secret. A secureproof may be referred to as a “proof of possession” or “proof ofknowledge” of a secret. Where at least a device-specific secret is aplurality of secrets, such as a plurality of challenge-response pairs, asecure proof may include an output that reveals the entirety of one ofthe plurality of secrets, but not all of the plurality of secrets; forinstance, secure proof may be a response contained in onechallenge-response pair. In an embodiment, proof may not be secure; inother words, proof may include a one-time revelation of at least adevice-specific secret, for instance as used in a singlechallenge-response exchange.

Still referring to FIG. 7 , secure proof may include a zero-knowledgeproof, which may provide an output demonstrating possession of a secretwhile revealing none of the secret to a recipient of the output;zero-knowledge proof may be information-theoretically secure, meaningthat an entity with infinite computing power would be unable todetermine secret from output. Alternatively, zero-knowledge proof may becomputationally secure, meaning that determination of secret from outputis computationally infeasible, for instance to the same extent thatdetermination of a private key from a public key in a public keycryptographic system is computationally infeasible. Zero-knowledge proofalgorithms may generally include a set of two algorithms, a proveralgorithm, or “P,” which is used to prove computational integrity and/orpossession of a secret, and a verifier algorithm, or “V” whereby a partymay check the validity of P. Zero-knowledge proof may include aninteractive zero-knowledge proof, wherein a party verifying the proofmust directly interact with the proving party; for instance, theverifying and proving parties may be required to be online, or connectedto the same network as each other, at the same time. Interactivezero-knowledge proof may include a “proof of knowledge” proof, such as aSchnorr algorithm for proof on knowledge of a discrete logarithm. in aSchnorr algorithm, a prover commits to a randomness r, generates amessage based on r, and generates a message adding r to a challenge cmultiplied by a discrete logarithm that the prover is able to calculate;verification is performed by the verifier who produced c byexponentiation, thus checking the validity of the discrete logarithm.Interactive zero-knowledge proofs may alternatively or additionallyinclude sigma protocols. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternativeinteractive zero-knowledge proofs that may be implemented consistentlywith this disclosure.

Still referring to FIG. 7 , alternatively, zero-knowledge proof mayinclude a non-interactive zero-knowledge, proof, or a proof whereinneither party to the proof interacts with the other party to the proof;for instance, each of a party receiving the proof and a party providingthe proof may receive a reference datum which the party providing theproof may modify or otherwise use to perform the proof. As anon-limiting example, zero-knowledge proof may include a succinctnon-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a“trusted setup” process creates proof and verification keys using secret(and subsequently discarded) information encoded using a public keycryptographic system, a prover runs a proving algorithm using theproving key and secret information available to the prover, and averifier checks the proof using the verification key; public keycryptographic system may include RSA, elliptic curve cryptography,ElGamal, or any other suitable public key cryptographic system.Generation of trusted setup may be performed using a secure multipartycomputation so that no one party has control of the totality of thesecret information used in the trusted setup; as a result, if any oneparty generating the trusted setup is trustworthy, the secretinformation may be unrecoverable by malicious parties. As anothernon-limiting example, non-interactive zero-knowledge proof may include aSuccinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledgeproof. In an embodiment, a ZK-STARKS proof includes a Merkle root of aMerkle tree representing evaluation of a secret computation at somenumber of points, which may be 1 billion points, plus Merkle branchesrepresenting evaluations at a set of randomly selected points of thenumber of points; verification may include determining that Merklebranches provided match the Merkle root, and that point verifications atthose branches represent valid values, where validity is shown bydemonstrating that all values belong to the same polynomial created bytransforming the secret computation. In an embodiment, ZK-STARKS doesnot require a trusted setup.

Still referring to FIG. 7 , zero-knowledge proof may include any othersuitable zero-knowledge proof. Zero-knowledge proof may include, withoutlimitation bulletproofs. Zero-knowledge proof may include a homomorphicpublic-key cryptography (hPKC)-based proof. Zero-knowledge proof mayinclude a discrete logarithmic problem (DLP) proof. Zero-knowledge proofmay include a secure multi-party computation (MPC) proof. Zero-knowledgeproof may include, without limitation, an incrementally verifiablecomputation (IVC). Zero-knowledge proof may include an interactiveoracle proof (IOP). Zero-knowledge proof may include a proof based onthe probabilistically checkable proof (PCP) theorem, including a linearPCP (LPCP) proof. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms ofzero-knowledge proofs that may be used, singly or in combination,consistently with this disclosure.

Still referring to FIG. 7 , in an embodiment, secure proof isimplemented using a challenge-response protocol. In an embodiment, thismay function as a one-time pad implementation; for instance, amanufacturer or other trusted party may record a series of outputs(“responses”) produced by a device possessing secret information, givena series of corresponding inputs (“challenges”), and store themsecurely. In an embodiment, a challenge-response protocol may becombined with key generation. A single key may be used in one or moredigital signatures as described in further detail below, such assignatures used to receive and/or transfer possession of crypto-currencyassets; the key may be discarded for future use after a set period oftime. In an embodiment, varied inputs include variations in localphysical parameters, such as fluctuations in local electromagneticfields, radiation, temperature, and the like, such that an almostlimitless variety of private keys may be so generated. Secure proof mayinclude encryption of a challenge to produce the response, indicatingpossession of a secret key. Encryption may be performed using a privatekey of a public key cryptographic system, or using a private key of asymmetric cryptographic system; for instance, trusted party may verifyresponse by decrypting an encryption of challenge or of another datumusing either a symmetric or public-key cryptographic system, verifyingthat a stored key matches the key used for encryption as a function ofat least a device-specific secret. Keys may be generated by randomvariation in selection of prime numbers, for instance for the purposesof a cryptographic system such as RSA that relies prime factoringdifficulty. Keys may be generated by randomized selection of parametersfor a seed in a cryptographic system, such as elliptic curvecryptography, which is generated from a seed. Keys may be used togenerate exponents for a cryptographic system such as Diffie-Helman orElGamal that are based on the discrete logarithm problem.

Still referring to FIG. 7 , as described above in some embodiments anavionic mesh network 700 may provide secure and/or encryptedcommunication at least in part by employing digital signatures. A“digital signature,” as used herein, includes a secure proof ofpossession of a secret by a signing device, as performed on providedelement of data, known as a “message.” A message may include anencrypted mathematical representation of a file or other set of datausing the private key of a public key cryptographic system. Secure proofmay include any form of secure proof as described above, includingwithout limitation encryption using a private key of a public keycryptographic system as described above. Signature may be verified usinga verification datum suitable for verification of a secure proof; forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic system, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

Still viewing FIG. 7 , in some embodiments, digital signatures may becombined with or incorporated in digital certificates. In oneembodiment, a digital certificate is a file that conveys information andlinks the conveyed information to a “certificate authority” that is theissuer of a public key in a public key cryptographic system. Certificateauthority in some embodiments contains data conveying the certificateauthority's authorization for the recipient to perform a task. Theauthorization may be the authorization to access a given datum. Theauthorization may be the authorization to access a given process. Insome embodiments, the certificate may identify the certificateauthority. The digital certificate may include a digital signature.

With continued reference to FIG. 7 , in some embodiments, a third partysuch as a certificate authority (CA) is available to verify that thepossessor of the private key is a particular entity; thus, if thecertificate authority may be trusted, and the private key has not beenstolen, the ability of an entity to produce a digital signature confirmsthe identity of the entity and links the file to the entity in averifiable way. Digital signature may be incorporated in a digitalcertificate, which is a document authenticating the entity possessingthe private key by authority of the issuing certificate authority andsigned with a digital signature created with that private key and amathematical representation of the remainder of the certificate. Inother embodiments, digital signature is verified by comparing thedigital signature to one known to have been created by the entity thatpurportedly signed the digital signature; for instance, if the publickey that decrypts the known signature also decrypts the digitalsignature, the digital signature may be considered verified. Digitalsignature may also be used to verify that the file has not been alteredsince the formation of the digital signature.

Now referring to FIG. 8 , an exemplary embodiment 800 of a flightcontroller 804 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 804 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 804may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 804 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 8 , flight controller 804may include a signal transformation component 808. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 808 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component808 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 808 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 808 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 808 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 8 , signal transformation component 808 may beconfigured to optimize an intermediate representation 812. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 808 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 808 may optimizeintermediate representation 812 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 808 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 808 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 804. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 808 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 8 , flight controller 804may include a reconfigurable hardware platform 816. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 816 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 8 , reconfigurable hardware platform 816 mayinclude a logic component 820. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 820 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 820 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 820 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 820 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 820 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 812. Logiccomponent 820 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 804. Logiccomponent 820 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 820 may beconfigured to execute the instruction on intermediate representation 812and/or output language. For example, and without limitation, logiccomponent 820 may be configured to execute an addition operation onintermediate representation 812 and/or output language.

In an embodiment, and without limitation, logic component 820 may beconfigured to calculate a flight element 824. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 824 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 824 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 824 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 8 , flight controller 804 may include a chipsetcomponent 828. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 828 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 820 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 828 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 820 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 828 maymanage data flow between logic component 820, memory cache, and a flightcomponent 832. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 832 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component832 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 828 may be configured to communicate witha plurality of flight components as a function of flight element 824.For example, and without limitation, chipset component 828 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 8 , flight controller 804may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 804 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 824. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 804 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 804 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 8 , flight controller 804may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 824 and a pilot signal836 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 836may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 836 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 836may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 836 may include an explicitsignal directing flight controller 804 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 836 may include an implicit signal, wherein flight controller 804detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 836 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 836 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 836 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 836 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal836 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 8 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 804 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 804.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 8 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 804 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 8 , flight controller 804 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 804. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 804 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 804 as a software update,firmware update, or corrected habit machine-learning model. For example,and without limitation autonomous machine learning model may utilize aneural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 8 , flight controller 804 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 8 , flight controller 804may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller804 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 804 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 804 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software's. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct flight components.

In an embodiment, and still referring to FIG. 8 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 832. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 8 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 804. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 812 and/or output language from logiccomponent 820, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 8 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 8 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 8 , flight controller 804 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 804 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 8 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 8 , flight controller may include asub-controller 840. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 804 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 840may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 840 may include any component of any flightcontroller as described above. Sub-controller 840 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 840may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 840 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 8 , flight controller may include aco-controller 844. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 804 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 844 mayinclude one or more controllers and/or components that are similar toflight controller 804. As a further non-limiting example, co-controller844 may include any controller and/or component that joins flightcontroller 804 to distributer flight controller. As a furthernon-limiting example, co-controller 844 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 804 to distributed flight control system. Co-controller 844may include any component of any flight controller as described above.Co-controller 844 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 8 , flightcontroller 804 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 804 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 9 , an exemplary embodiment of a machine-learningmodule 900 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 904 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 908 given data provided as inputs 912;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 9 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 904 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 904 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 904 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 904 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 904 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 904 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data904 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 9 ,training data 904 may include one or more elements that are notcategorized; that is, training data 904 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 904 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 904 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 904 used by machine-learning module 900 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 9 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 916. Training data classifier 916 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 900 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 904. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 9 , machine-learning module 900 may beconfigured to perform a lazy-learning process 920 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 904. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 904 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 9 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 924. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 924 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 924 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 904set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 9 , machine-learning algorithms may include atleast a supervised machine-learning process 928. At least a supervisedmachine-learning process 928, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 904. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process928 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 9 , machine learning processes may include atleast an unsupervised machine-learning processes 932. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 9 , machine-learning module 900 may be designedand configured to create a machine-learning model 924 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 9 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 10 , an exemplary augmented reality (AR) method1000 of simulated operation of an electric vertical take-off and landing(eVTOL) aircraft is shown. At step 1005, a computing device may operatea flight simulator to simulate flight in an environment. Computingdevice may include any computing device described in this disclosure,including for example with reference to FIGS. 1-9 and 11 . Flightsimulator may include any flight simulator described in this disclosure,including for example with reference to FIGS. 1-9 . Environment mayinclude any environment described in this disclosure, including forexample with reference to FIGS. 1-9 .

Continuing with reference to FIG. 10 , at step 1010, computing devicemay simulate at least a virtual representation interactive with flightsimulator. At least a virtual representation may include an aircraftdigital twin representing at least an aircraft component of an electricvertical take-off and landing (eVTOL) aircraft. At least a virtualrepresentation may include any virtual representation described in thisdisclosure, for example with reference to FIGS. 1-9 . An aircraftdigital twin may include any digital twin described in this disclosure,including for example with reference to FIGS. 1-9 . At least an aircraftcomponent may include any aircraft component described in thisdisclosure, including for example with reference to FIGS. 1-9 . eVTOLaircraft may include any virtual eVTOL aircraft described in thisdisclosure, including for example with reference to FIGS. 1-9 . Inembodiments, at least a virtual representation may include a virtualcontroller area network. In some embodiments, aircraft digital twin mayinclude a flight controller model. In some embodiments, at least anaircraft component may include an eVTOL aircraft. eVTOL aircraft mayinclude any eVTOL aircraft described in this disclosure, including forexample with reference to FIGS. 1-9 .

Continuing with reference to FIG. 10 , at step 1015, a displaycommunicatively connected to the computing device may display at least avirtual representation. Display may include any display described inthis disclosure, for instance in reference to FIGS. 1-9 and 11 .

Continuing with reference to FIG. 10 , at step 1020, a mesh network maycommunicatively connect at least an aircraft component and computingdevice. Mesh network may include any network described in thisdisclosure, including for example with reference to FIGS. 1-9 . At step1025, mesh network may communicate encrypted data. Encrypted data mayinclude any encrypted data described in this disclosure, including forexample with reference to FIGS. 1-9 . In some embodiments, mesh networkmay include an intra-aircraft network.

Still referring to FIG. 10 , in some embodiments, method 1000 mayadditionally include transmitting, using computing device, encrypteddata to at least an aircraft component. In some embodiments, method 100may additionally include receiving, using computing device, encrypteddata from at least an aircraft component.

Still referring to FIG. 10 , in some embodiments, method 1000 mayadditionally include performing, using a physical cockpit of a simulatormodule, at least a simulated flight mission; interfacing, using at leasta pilot control of the simulator module, with a user; and detecting,using at least a sensor communicatively connected to computing device, auser interaction with the at least a pilot control. Simulator module mayinclude any simulator module described in this disclosure, including forexample with reference to FIGS. 1-9 . Physical cockpit may include anyphysical cockpit described in this disclosure, including for examplewith reference to FIGS. 1-9 . Pilot control may include any pilotcontrol described in this disclosure, including for example withreference to FIGS. 1-9 . At least a sensor may include any sensordescribed in this disclosure, for example with reference to FIGS. 1-9 .In some cases, at least a virtual representation comprises a simulatordigital twin of at least a portion of simulator module. Simulatordigital twin may include any digital twin described in this disclosure,including for example with reference to FIGS. 1-9 . In some cases,method 1000 additionally includes communicatively connecting, using meshnetwork, simulator module with the at least an aircraft component andcomputing device.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 11 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1100 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1100 includes a processor 1104 and a memory1108 that communicate with each other, and with other components, via abus 1112. Bus 1112 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1104 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1104 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1104 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 1108 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1116 (BIOS), including basic routines thathelp to transfer information between elements within computer system1100, such as during start-up, may be stored in memory 1108. Memory 1108may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1120 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1108 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1100 may also include a storage device 1124. Examples ofa storage device (e.g., storage device 1124) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1124 may beconnected to bus 1112 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1124 (or one or more components thereof) may be removably interfacedwith computer system 1100 (e.g., via an external port connector (notshown)). Particularly, storage device 1124 and an associatedmachine-readable medium 1128 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1100. In one example,software 1120 may reside, completely or partially, withinmachine-readable medium 1128. In another example, software 1120 mayreside, completely or partially, within processor 1104.

Computer system 1100 may also include an input device 1132. In oneexample, a user of computer system 1100 may enter commands and/or otherinformation into computer system 1100 via input device 1132. Examples ofan input device 1132 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1132may be interfaced to bus 1112 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1112, and any combinations thereof. Input device 1132may include a touch screen interface that may be a part of or separatefrom display 1136, discussed further below. Input device 1132 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1100 via storage device 1124 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1140. A networkinterface device, such as network interface device 1140, may be utilizedfor connecting computer system 1100 to one or more of a variety ofnetworks, such as network 1144, and one or more remote devices 1148connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1144, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1120, etc.) may be communicated to and/or fromcomputer system 1100 via network interface device 1140.

Computer system 1100 may further include a video display adapter 1152for communicating a displayable image to a display device, such asdisplay device 1136. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1152 and display device 1136 maybe utilized in combination with processor 1104 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1100 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1112 via a peripheral interface 1156.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for simulated operation of an electricvertical take-off and landing (eVTOL) aircraft, comprising: at least anaircraft component of an electric vertical take-off and landing (eVTOL)aircraft, wherein the aircraft component includes a battery of theeVTOL; a computing device configured to: simulate flight in anenvironment; perform a simulated flight mission using an aircraftdigital twin representing a virtual simulation of the at least anaircraft component, wherein: the aircraft digital twin comprises abattery model configured to predict an energy efficiency of the batteryof the eVTOL; and the aircraft digital twin is configured to beiteratively updated based on at least a measured state data of the atleast an aircraft component of the eVTOL, wherein the at least ameasured state data represents at least a measured state of the batteryof the eVTOL; encrypt and decrypt encrypted data comprising the at leasta measured state datum; and iteratively update the aircraft digital twinbased on the at least a measured state datum of the at least an aircraftcomponent of the eVTOL; and a mesh network configured to:communicatively connect the at least an aircraft component and thecomputing device; and communicate the encrypted data between the atleast an aircraft component and the computing device, the encrypted datacomprising the at least a measured state datum.
 2. The system of claim1, wherein the aircraft digital twin comprises a sensor model.
 3. Thesystem of claim 1, wherein the aircraft digital twin comprises a thermalmodel.
 4. The system of claim 1, wherein the aircraft digital twincomprises a flight controller model.
 5. The system of claim 1, whereinthe aircraft digital twin comprises a flight component model.
 6. Thesystem of claim 1, wherein the aircraft digital twin comprises amanufacturing model.
 7. The system of claim 2, further comprising: asimulator module communicative with the computing device and comprising:a physical cockpit configured to be used for performing the at least asimulated flight mission; at least a pilot control configured tointerface with a user; and at least a sensor communicatively connectedto the computing device and configured to detect a user interaction withthe at least a pilot control.
 8. The system of claim 7, wherein the atleast a virtual representation comprises a simulator digital twin of atleast a portion of the simulator module.
 9. The system of claim 7,wherein the mesh network is further configured to communicativelyconnect the simulator module with the at least an aircraft component andthe computing device.
 10. The system of claim 1, wherein the at least anaircraft component comprises the eVTOL aircraft.
 11. A method ofsimulated operation of an electric vertical take-off and landing (eVTOL)aircraft, comprising: simulating, using a computing device, a flight ofthe eVTOL in an environment performing, using the computing device, asimulated flight mission using an aircraft digital twin representing avirtual simulation of at least an aircraft component of an electricvertical take-off and landing (eVTOL) aircraft, wherein the aircraftdigital twin comprises a battery model configured to predict an energyof the battery of the eVTOL; encrypting and decrypting, using thecomputing device, encrypted data comprising at least a measured statedatum, wherein the at least a measured state datum represents at least ameasured state of the battery of the eVTOL; iteratively updating, usingthe computing device, the aircraft digital twin based on the at least ameasured state datum; communicatively connecting, using a mesh network,the at least an aircraft component and the computing device; andcommunicating, using the mesh network, encrypted data between the atleast an aircraft component and the computing device comprising the atleast a measured state datum.
 12. The method of claim 11, wherein theaircraft digital twin comprises a sensor model.
 13. The method of claim11, wherein the aircraft digital twin comprises a thermal model.
 14. Themethod of claim 11, wherein the aircraft digital twin comprises a flightcontroller model.
 15. The method of claim 11, wherein the aircraftdigital twin comprises a flight component model.
 16. The method of claim11, wherein the aircraft digital twin comprises a manufacturing model.17. The method of claim 12, the method further comprising: performing,using a physical cockpit of a simulator module, the at least a simulatedflight mission; interfacing, using at least a pilot control of thesimulator module, with a user; and detecting, using at least a sensorcommunicatively connected to the computing device, a user interactionwith the at least a pilot control.
 18. The method of claim 17, whereinthe at least a virtual representation comprises a simulator digital twinof at least a portion of the simulator module.
 19. The method of claim17, further comprising: communicatively connecting, using the meshnetwork, the simulator module with the at least an aircraft componentand the computing device.
 20. The method of claim 11, wherein the atleast an aircraft component comprises the eVTOL aircraft.